reinforcement learning

Learning from What You Retrieve: Online RL Fine-Tuning for Semantic Retrieval

This work proposes PAO (Positive-Advantage-Only), a selective RL optimization method that selectively applies gradient updates only to retrieved items with positive advantages, effectively pulling query embed- dings toward high-reward regions while preserving global topo- logical stability.

MedAgent-R1: Faithfulness-Aware Reinforcement Learning for Evidence-Grounded Medical Reasoning

This work identifies a systematic failure mode in RL-trained retrieval agents: outcome-only rewards improve accuracy while degrading faithfulness, a phenomenon the authors term confident hallucination, and addresses this with a faithfulness-gated reward design.

RecRec: Latent Interests Recursive Reasoning for Sequential Recommendation

Rec is proposed, an RL-free framework that decouples reasoning from prediction, overcoming the fixed d-dimensional state bottleneck of prior methods, and points to a decoupled, multi-vector recipe that unleashes latent reasoning from the single-state bottleneck of prior methods.

Quantum gated recurrent neural networks for continuous dynamic temporal decision-making

A novel policy network, quantum gated recurrent neural network (QGRNN), which incorporates quantum superposition encoding and cyclic entanglement structures to achieve more efficient state-space exploration and employs a quantum–classical hybrid residual design that effectively integrates quantum measurement outcomes w...

Automated Vulnerability Patching in Legacy Code Using LLMs and Multi-AI Agents

While multi-agent LLM pipelines show real promise for legacy code specifically, since their capacity for autonomous context reconstruction and iterative, feedback-driven refinement directly addresses the sparse documentation and thin test coverage that define legacy environments, the literature to date has been validat...

Not Just Reason, Not Just Scan: Reinforcement Learning for Proactive Scientific Error Verification over Academic Paper

This work presents VERA-RL, a reinforcement-learning formulation for scientific error detection over academic papers, and constructs VERA-13K, a 12,900-sample dataset organized into 4,300 matched chains, covering 6 scientific-error categories across the research workflow and broad natural-science domains.

SAFE-G: Structure-aware Faithful Evidence-guided Generation for Knowledge-based Visual Question Answering

This work proposes SAFE-G, a Structure-Aware Faithful Evidence-guided Generation framework, which enables precise evidence localization and trustworthy reasoning and introduces a reinforcement learning strategy with an evidence-grounded reward that assigns credit to correct answers only when the selected evidence is co...

AI-Powered Anomaly Detection in Cloud-Based Applications

The findings suggest that AI-powered anomaly detection significantly strengthens observability and security in cloud-based applications, enabling proactive threat mitigation and operational optimization in increasingly complex distributed environments.

Numerical study on hydrogen-air explosion dynamic characteristics with deep reinforcement learning

Robust low-carbon ship routing under weather uncertainty: a distributional reinforcement learning approach

LumiCharge: Spherical Harmonic Convolutional Networks for Atomic Charge Prediction in Drug Discovery.

This work proposes LumiCharge, a novel atomic charge prediction framework that incorporates high-order spherical harmonics convolutions and explicitly models multibody interactions, and demonstrates exceptional extrapolation capability and robustness across molecules of varying sizes, effectively overcoming the limitat...

PepBAN: A Deep Learning Framework with Bilinear Attention and Adversarial Learning for Peptide-Protein Interaction Prediction

PepBAN is introduced, a deep learning framework for modeling PepPI predictions that effectively learns the pattern of pairwise local interactions, enables the identification of key residues participating in the peptide-protein interactions, and offers an intuitive approach to interpret the underlying mechanisms of PepP...